Note:- Only publicly available data can be worked upon

APISCRAPY collects and organizes data from Zillow's massive database, whether it's property characteristics, market trends, pricing histories, or more. Because of APISCRAPY's first-rate data extraction services, tracking property values, examining neighborhood trends, and monitoring housing market variations become a straightforward and efficient process.

APISCRAPY's Zillow real estate data scraping service offers numerous advantages for individuals and businesses seeking valuable insights into the real estate market. Here are key benefits associated with their advanced data extraction technology:

1. Real-time Zillow Real Estate Data: Users can access real-time data from Zillow, providing timely updates on property listings, market dynamics, and other critical factors. This real-time information is invaluable for making informed decisions in a fast-paced real estate environment.

2. Data Customization: APISCRAPY allows users to customize the data extraction process, tailoring it to their specific needs. This flexibility ensures that the extracted Zillow real estate data aligns precisely with the user's requirements.

3. Precision and Accuracy: The advanced algorithms utilized by APISCRAPY enhance the precision and accuracy of the extracted Zillow real estate data. This reliability is crucial for making well-informed decisions related to property investments and market trends.

4. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can access the desired Zillow real estate data without unnecessary delays.

5. User-friendly Interface: APISCRAPY provides a user-friendly interface, making it accessible for individuals and businesses to navigate and utilize the Zillow real estate data scraping service with ease.

APISCRAPY provides real-time real estate market data drawn from Zillow, ensuring that consumers have access to the most up-to-date and comprehensive real estate insights available. Our real-time real estate market data services aren't simply a game changer in today's dynamic real estate landscape; they're an absolute requirement.

Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

APISCRAPY's data extraction is a key element, and it is an automated and smooth procedure that is at the heart of the platform's operation. Our platform gathers Zillow real estate data quickly and offers it in an easily consumable format with the click of a button.

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Gain access to comprehensive real estate data from all major real estate property listing sites in the USA, Canada, UK, and other countries with our expert real estate scraping service. Unlock valuable insights from Zillow, Realtor.com, Trulia, Redfin, and more.

We create tailor-made solutions for every customer, so there are no limits to how we can customize your scraper. You don't have to worry about buying and maintaining complex and expensive software, or hiring developers.

You can get the data on a one-time or recurring (based on your needs) basis.

Get the data in any format and to any destination you need: Excel, CSV, JSON, XML, S3, GCP, or any other.

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Note:- Only publicly available real estate data can be worked upon.

Discover the world of property insights with APISCRAPY's user-friendly services – Realtor Property Data, Realtor Data, and Realtor API. Designed for ease of use, our platform allows anyone, from real estate professionals to researchers and businesses, to effortlessly access publicly available property listings and Property owner Data.

Our Realtor Property Data service provides comprehensive details on property listings, while Realtor API ensures easy integration for streamlined access. Additionally, we offer Zillow Property Data, enriching your property insights with information from one of the leading property platforms.

Key Features:

Realtor Property Data: Dive into detailed property listings effortlessly with our user-friendly platform.

Realtor API Integration: Seamlessly integrate our Realtor API into your systems for easy access to property data.

Zillow Property Data: Enrich your property insights with data from Zillow, one of the leading property platforms.

Publicly Available Property Listings: APISCRAPY ensures access to publicly available property listings, making property data easily accessible.

Easy Integration: Our platform is designed for simplicity, allowing for easy integration into your existing systems.

Whether you're a real estate professional, researcher, or business looking for straightforward access to property information, APISCRAPY's services cater to your needs. Choose us for simple and efficient property data services, where ease of use and accessibility come together for your convenience.

Explore the Redfin Canada Real Estate Data, last extracted in June 2022 and available in CSV format. This robust dataset contains over 100,000 records, offering detailed insights into the Canadian housing market.

It includes comprehensive data on property listings, prices, square footage, and more across various cities and provinces.

Ideal for real estate analysis, market trend research, and investment planning, this dataset is a valuable resource for professionals seeking in-depth understanding of the Canadian real estate landscape.

Where is the data from?

The data was scraped from Immoscout24, the biggest real estate platform in Germany. Immoscout24 has listings for both rental properties and homes for sale, however, the data only contains offers for rental properties. The scraping process is described in this blog post and the corresponding code for scraping and minimal processing afterwards can be found in this Github repo. At a given time, all available offers were scraped from the site and saved. This process was repeated three times, so the data set contains offers from the dates 2018-09-22, 2019-05-10 and 2019-10-08.

Content

The data set contains most of the important properties, such as living area size, the rent, both base rent as well as total rent (if applicable), the location (street and house number, if available, ZIP code and state), type of energy etc. It also has two variables containing longer free text descriptions: description with a text describing the offer and facilities describing all available facilities, newest renovation etc. The date column was added to give the time of scraping.

Inspiration

Did rents increase over time? Which areas are the most expensive? Which areas saw the largest increase, which areas became cheaper? Are there any duplicates? How many? What could be gained from a text analysis of the free text variables?

Acknowledgements

The data belongs to www.immobilienscount24.de and is for research purposes only. The data was created with .

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

Property Listings in Kuala Lumpur

This is the tabular result of scraping a property listing website for properties for sale in Kuala Lumpur, Malaysia. Only the overview page was scraped so individual property details are scarce.

This data set includes a snapshot of the state of the rental housing market in Catalonia (Spain) on the 6th April 2020. The data was obtained using a web scraping tool on the real estate website www.tucasa.com. The web scraper collected data on price, square metres, price per square metre, number of rooms, number of bathrooms, neighbourhood and city; in the four main cities of Catalonia (Barcelona, Girona, Lleida and Tarragona).

The Web Scraping Market is Segmented by Solution (Software, Services), Deployment Type (Cloud, On-Premise), End-User Industry (BFSI, Retail and E-Commerce, Real Estate, Manufacturing, Government, Healthcare, Advertising and Media, and More), Use Case (Data Scaping / ETL, Price and Competitive Monitoring, and More), and Geography.

Explore the Redfin Canada Properties Dataset, available in CSV format and extracted in April 2022. This comprehensive dataset offers detailed insights into the Canadian real estate market, including property listings, prices, square footage, number of bedrooms and bathrooms, and more. Covering various cities and provinces, it’s ideal for market analysis, investment research, and financial modeling.

Key Features:

• Property Details: Includes crucial data such as listing price, property type, square footage, number of bedrooms and bathrooms, and more.
• Geo-Location Data: Provides geographical coordinates, allowing for spatial analysis and mapping.
• Market Trends: Offers historical data to analyze price trends and market fluctuations.

Who Can Use This Dataset:

• Real Estate Professionals: Evaluate market trends and property values to better advise clients or guide investment decisions.
• Investors: Analyze the Canadian housing market to identify investment opportunities and potential returns.
• Data Analysts and Researchers: Use this dataset to study market dynamics, urban development, or economic factors influencing the real estate sector.
• Financial Analysts: Incorporate the data into financial models to forecast market behavior and investment outcomes.

Download the Redfin Canada Properties Dataset to access valuable information on the Canadian housing market, perfect for anyone involved in real estate, finance, or data analysis.

The global data scraping service market size was valued at USD 2.20 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 11.5% from 2022 to 2030. The growth of the market is attributed to the increasing adoption of data scraping techniques by businesses to gain valuable insights from unstructured data. Additionally, the rising popularity of big data and artificial intelligence (AI) is driving the demand for data scraping services. The key drivers of the market include the growing need for data-driven decision-making, the increasing adoption of cloud-based data storage solutions, and the advancements in machine learning and AI technologies. The market is segmented by application into e-commerce, retail, real estate, finance, and others. The e-commerce segment held the largest market share in 2021 and is expected to continue to dominate the market over the forecast period. The retail and real estate segments are also expected to witness significant growth during the forecast period.

Dataset

This dataset was created by Tyler Tresslar

Contents

The global web scraper software market, valued at $7241.5 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid expansion of e-commerce, big data analytics, and the increasing need for real-time data across various sectors, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers. The rising demand for automated data extraction from websites for market research, price comparison, lead generation, and competitive analysis is significantly boosting market adoption. Furthermore, advancements in AI and machine learning are enhancing the capabilities of web scrapers, enabling more efficient and accurate data retrieval. The diverse application segments, including retail & e-commerce, advertising & media, finance, and real estate, all contribute to the market's expansive potential. While challenges such as website structure changes and legal constraints related to data scraping exist, the overall market outlook remains positive. The increasing sophistication of web scraping tools and the development of robust solutions that address legal and ethical concerns are mitigating these restraints. The market segmentation reveals a diverse landscape. General-purpose web scrapers cater to a broad user base, while focused scrapers target specific data types and websites. Incremental scrapers efficiently update existing datasets, and deep web scrapers access data beyond standard search engines. The application-based segmentation underscores the versatility of the technology, with e-commerce and advertising and media sectors being significant contributors. Leading players like Apify, Import.io, and Octoparse are driving innovation and competition, contributing to a robust and evolving market. Regional analysis suggests significant market presence across North America and Europe, followed by a growing presence in the Asia-Pacific region. The continued development of robust, ethical, and user-friendly web scraping solutions will be key to unlocking the full potential of this rapidly expanding market.

South Carolina Real Estate Dataset 2025

Dataset Overview

This comprehensive real estate dataset contains over 5,000 property listings from South Carolina, collected in 2025 from Realtor.com using apify api. The dataset captures diverse property types including single-family homes, condominiums, land parcels, townhomes, and other residential properties. This dataset provides a rich snapshot of South Carolina's real estate market suitable for predictive modeling, market analysis, and investment research.

Data Science Applications

• Price Prediction Models: Build regression models (Random Forest, XGBoost, Neural Networks) to predict property values based on size, location, bedrooms, and age
• Property Type Classification: Develop multi-class classifiers to categorize properties based on physical characteristics
• Market Segmentation: Apply clustering algorithms (K-means, DBSCAN) to identify distinct property segments and price brackets
• Time Series Analysis: Analyze construction trends and property age distributions to forecast future development patterns
• Investment Opportunity Detection: Create anomaly detection models to identify undervalued properties or outliers
• Feature Engineering: Generate derived features like price per square foot, bathroom-to-bedroom ratios for enhanced model performance

Column Descriptors

• type: Primary property category (single_family, condos, land, townhomes, multi_family, farm)
• sub_type: Detailed property classification (condo, townhouse, co_op)
• sqft: Property size in square feet
• baths: Number of bathrooms (decimal values indicate half baths)
• beds: Number of bedrooms
• stories: Number of floors/stories in the property
• year_built: Construction year of the property
• listPrice: Property listing price in USD

Ethically Obtained Data

This dataset was ethically scraped from publicly available listings on Realtor.com and is provided strictly for educational and learning purposes only. The data collection complied with ethical web scraping practices and contains only publicly accessible information. Users should utilize this dataset exclusively for academic research, educational projects, and learning data science techniques. Any commercial use is strictly prohibited.

Discover the ultimate web scraping tool of 2024 for unlocking valuable insights. Effortlessly extract web data for ecommerce, real estate, and beyond. Harness the power of web scraping to drive informed decision-making and gain a competitive edge.

The web scraping software market is rapidly expanding, with a market size of USD 31940 million in 2025 and a projected CAGR of 34.2% from 2025 to 2033. This growth is driven by the rising demand for data-driven insights and the need for efficient and scalable web data extraction solutions. Key drivers include the increasing adoption of cloud-based solutions, the growth of e-commerce and online marketplaces, and the need for real-time data extraction for business intelligence. Major trends in the web scraping software market include the adoption of artificial intelligence and machine learning technologies for advanced data extraction capabilities, the emergence of low-code and no-code platforms for easier deployment, and the growing focus on data privacy and compliance. However, concerns over data security, legal implications, and ethical considerations pose potential restraints to the market growth. The market is segmented by type (cloud-based and on-premises) and application (financial analysis, travel and hospitality, real estate, jobs and human capital, and others), with prominent companies including Import.io, Octopus Data, Mozenda, Diffbot, Scrapinghub, and ParseHub.